Author
Listed:
- Gautam A. Kavuri
(University of Colorado
National Institute of Standards and Technology)
- Jasper Palfree
(University of Colorado
National Institute of Standards and Technology)
- Dileep V. Reddy
(University of Colorado
National Institute of Standards and Technology)
- Yanbao Zhang
(Oak Ridge National Laboratory)
- Joshua C. Bienfang
(National Institute of Standards and Technology and University of Maryland)
- Michael D. Mazurek
(University of Colorado
National Institute of Standards and Technology)
- Mohammad A. Alhejji
(University of New Mexico)
- Aliza U. Siddiqui
(University of Colorado)
- Joseph M. Cavanagh
(National Institute of Standards and Technology
University of California)
- Aagam Dalal
(National Institute of Standards and Technology)
- Carlos Abellán
(Quside Technologies)
- Waldimar Amaya
(Quside Technologies)
- Morgan W. Mitchell
(The Barcelona Institute of Science and Technology
ICREA - Institució Catalana de Recerca i Estudis Avançats)
- Katherine E. Stange
(University of Colorado)
- Paul D. Beale
(University of Colorado)
- Luís T. A. N. Brandão
(Strativia (NIST Cryptographic Technology Group))
- Harold Booth
(National Institute of Standards and Technology)
- René Peralta
(National Institute of Standards and Technology)
- Sae Woo Nam
(University of Colorado
National Institute of Standards and Technology)
- Richard P. Mirin
(National Institute of Standards and Technology)
- Martin J. Stevens
(National Institute of Standards and Technology)
- Emanuel Knill
(University of Colorado
University of Colorado
National Institute of Standards and Technology)
- Lynden K. Shalm
(University of Colorado
National Institute of Standards and Technology
University of Colorado)
Abstract
The unpredictability of random numbers is fundamental to both digital security1,2 and applications that fairly distribute resources3,4. However, existing random number generators have limitations—the generation processes cannot be fully traced, audited and certified to be unpredictable. The algorithmic steps used in pseudorandom number generators5 are auditable, but they cannot guarantee that their outputs were a priori unpredictable given knowledge of the initial seed. Device-independent quantum random number generators6–9 can ensure that the source of randomness was unknown beforehand, but the steps used to extract the randomness are vulnerable to tampering. Here we demonstrate a fully traceable random number generation protocol based on device-independent techniques. Our protocol extracts randomness from unpredictable non-local quantum correlations, and uses distributed intertwined hash chains to cryptographically trace and verify the extraction process. This protocol forms the basis for a public traceable and certifiable quantum randomness beacon that we have launched10. Over the first 40 days of operation, we completed the protocol 7,434 out of 7,454 attempts—a success rate of 99.7%. Each time the protocol succeeded, the beacon emitted a pulse of 512 bits of traceable randomness. The bits are certified to be uniform with error multiplied by actual success probability bounded by 2−64. The generation of certifiable and traceable randomness represents a public service that operates with an entanglement-derived advantage over comparable classical approaches.
Suggested Citation
Gautam A. Kavuri & Jasper Palfree & Dileep V. Reddy & Yanbao Zhang & Joshua C. Bienfang & Michael D. Mazurek & Mohammad A. Alhejji & Aliza U. Siddiqui & Joseph M. Cavanagh & Aagam Dalal & Carlos Abell, 2025.
"Traceable random numbers from a non-local quantum advantage,"
Nature, Nature, vol. 642(8069), pages 916-921, June.
Handle:
RePEc:nat:nature:v:642:y:2025:i:8069:d:10.1038_s41586-025-09054-3
DOI: 10.1038/s41586-025-09054-3
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